With the popularity of intelligent operation systems, such as Android (Android), IOS, more and more applications are loaded on terminal devices. Hence, how to better provide application download services for the terminal device is more important. At present, an application download platform, such as an application store, is a major way for the terminal device to download applications. The application download platform may sort all applications and present the applications on an application recommendation interface. Generally, the application recommendation interface includes a home page of the application download platform or a class interface for each class. FIG. 1 is a schematic diagram of an application recommendation interface. The application download platform may sort all applications and present the applications on the home page. Apparently, in the case of the class interface, the application download platform may also classify applications, sort applications of each class, and present the applications on the class interface.
In recommending applications on the application recommendation interface of the application download platform, a recommendation position of an application (i.e., an ordinal number of the application) greatly affects a user's interest in the application. Hence, how to appropriately set the recommendation position of the application is a difficult problem in recommending applications. The current application recommendation method is mainly based on download times of the application to set the recommended position of the application. That is, a recommendation position of an application with high download times is before a recommendation position of an application with low download times.
However, an application in a front recommendation position should be an application that has high popularity and prevalence in a user use level. With the existing application recommendation method, there may be a case that high download times of an application represents the popularity and prevalence in a user use level. Especially, if an application has make-up download times, the application which has make-up download times but has low popularity and prevalence in a user use level, may still occupy a front recommendation position for a long time, and thus an inappropriate application is recommended. As a result, application recommendation positions set by the existing application recommendation method are very likely to mismatch the popularity and prevalence at a user level, resulting in inappropriate application recommendation. Therefore, how to improve the appropriateness of the application recommendation position arrangement and thus improve the appropriateness of the application recommendation is a problem that needs to be considered.